To address the aforementioned challenges, this paper proposes a multi-graph, multi-source representation learning framework, called Region2Vec, to carry out highly effective urban region profiling. We notice that POI data has many inherent attributes (e.g., category, subclass). At the same time, recent research in graph embedding tends to take a graph as the input and leverage the auxiliary information to facilitate more effective embedding [1]}, [2]}. Thus, we treat POI attributes as the side information and construct a knowledge graph for POIs to discover region-wise inherent properties. In addition, we also incorporate geographical neighborhood into the framework as the geographical contextual signals since adjacent regions naturally show direct correlations according to the First Law of Geography [3]}. By incorporating multi-source data including global-level human mobility, local-level geographical neighborhood, and region-wise POI side information, the urban spaces are comprehensively depicted by "man-land-dynamic-static", which are the classical four dimensions in Geography [4]}. Then, through mobility pattern similarity analysis, topology analysis, and constructing knowledge graph, we can then encode accessibility, vicinity, functionality correlations among regions by constructing graphs. To better propagate information for every single data source, the graph attention network is employed. Furthermore, to promote cooperation of different graph representations, a multi-graph fusion module with some designed learning objectives is proposed to model the underlying correlations among graphs in a joint manner. The contributions of this paper can be summarized as follows.
